Saurabh’s Original Framework
AI Reliability Pyramid
Reliable AI is built in layers, not by one perfect prompt.
Developed by Saurabh Jaiswal as part of the AI for Real Work methodology, this framework helps teams reduce hallucinations and improve trust in real-world AI systems.
Core idea
AI reliability does not come from using a bigger model alone. It comes from layered design: clear instructions, trusted context, retrieval, validation, observability, and human oversight.
The 5 layers
Layer 1
Prompt Clarity
Make the task, role, format, and constraints clear.
Layer 2
Trusted Context
Ground the AI with reliable data and business information.
Layer 3
Retrieval
Use RAG and knowledge systems to reduce unsupported answers.
Layer 4
Validation
Check outputs using rules, tests, tools, or secondary review.
Layer 5
Human Review
Keep human judgment in high-risk or high-impact decisions.
Why it matters
In enterprise AI, confidence without verification is dangerous. Reliability must be designed into the workflow from the beginning.
Real-world use
Use this for AI copilots, legal review assistants, finance workflows, healthcare support tools, compliance systems, and customer-facing AI.
Final takeaway
Trustworthy AI is not a feature. It is an architecture.